code: def test_misclassification(backend_default):
      NervanaObject.be.bsz = 3
      outputs = np.array(
          [[0.25, 0.99, 0.33], [0.5, 0.005, 0.32], [0.25, 0.005, 0.34]]
      )
      targets = np.array([[0, 1, 0], [1, 0, 1], [0, 0, 0]])
      expected_result = np.ones((1, 1)) / 3.0
      compare_metric(
          Misclassification(),
          outputs,
          targets,
          expected_result,
          tol=1e-7,
      )
  
imports: import itertools
  import numpy
  from neon import NervanaObject
  from neon.backends import gen_backend
  from neon.transforms import CrossEntropyBinary, CrossEntropyMulti, SumSquared, MeanSquared, Misclassification, PrecisionRecall, SmoothL1Loss, SquareHingeLoss
label_desc: Use image node_id 16 for calling a global function with example usage: test_misclassification(backend_default) without return types